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Data-Driven Parameter Calibration in Additive Manufacturing for Construction: An Introduction to Learning by Printing

Year: 2025

Type: conference-paper

Abstract: This paper presents the Learning by Printing framework, a data-driven, closed-loop system using Fabrication Information Modeling to optimize extrusion-based additive manufacturing in construction, achieving over 95% prediction accuracy and improved performance through parameter calibration.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17052168

Authors: Bettermann, Luca, Slepicka, Martin, Esser, Sebastian, Borrmann, André

Institution: Technical University of Munich

Language: English

Topic: Innovations in Concrete and Construction Materials

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: European Commission